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2020 – today
- 2025
[i12]Dan Braun, Lucius Bushnaq, Stefan Heimersheim, Jake Mendel, Lee Sharkey:
Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-based Parameter Decomposition. CoRR abs/2501.14926 (2025)- [i11]Lee Sharkey, Bilal Chughtai, Joshua Batson, Jack Lindsey, Jeff Wu, Lucius Bushnaq, Nicholas Goldowsky-Dill, Stefan Heimersheim, Alejandro Ortega, Joseph Isaac Bloom, Stella Biderman, Adrià Garriga-Alonso, Arthur Conmy, Neel Nanda, Jessica Rumbelow, Martin Wattenberg, Nandi Schoots, Joseph Miller, Eric J. Michaud, Stephen Casper, Max Tegmark, William Saunders, David Bau, Eric Todd, Atticus Geiger, Mor Geva, Jesse Hoogland, Daniel Murfet, Tom McGrath:
Open Problems in Mechanistic Interpretability. CoRR abs/2501.16496 (2025) - [i10]Patrick Leask, Bart Bussmann, Michael T. Pearce, Joseph Isaac Bloom, Curt Tigges, Noura Al Moubayed, Lee Sharkey, Neel Nanda:
Sparse Autoencoders Do Not Find Canonical Units of Analysis. CoRR abs/2502.04878 (2025) - 2024
- [c4]Stephen Casper
, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas A. Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell:
Black-Box Access is Insufficient for Rigorous AI Audits. FAccT 2024: 2254-2272 - [c3]Robert Huben, Hoagy Cunningham, Logan Riggs, Aidan Ewart, Lee Sharkey:
Sparse Autoencoders Find Highly Interpretable Features in Language Models. ICLR 2024 - [c2]Dan Braun, Jordan Taylor, Nicholas Goldowsky-Dill, Lee Sharkey:
Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning. NeurIPS 2024 - [i9]Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas Alexander Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell:
Black-Box Access is Insufficient for Rigorous AI Audits. CoRR abs/2401.14446 (2024) - [i8]Dan Braun, Jordan Taylor, Nicholas Goldowsky-Dill, Lee Sharkey:
Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning. CoRR abs/2405.12241 (2024) - [i7]Michael T. Pearce, Thomas Dooms, Alice Rigg, José Oramas M., Lee Sharkey:
Bilinear MLPs enable weight-based mechanistic interpretability. CoRR abs/2410.08417 (2024) - [i6]Kola Ayonrinde, Michael T. Pearce, Lee Sharkey:
Interpretability as Compression: Reconsidering SAE Explanations of Neural Activations with MDL-SAEs. CoRR abs/2410.11179 (2024) - 2023
- [i5]Lee Sharkey:
A technical note on bilinear layers for interpretability. CoRR abs/2305.03452 (2023) - [i4]Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, Lee Sharkey:
Sparse Autoencoders Find Highly Interpretable Features in Language Models. CoRR abs/2309.08600 (2023) - 2022
- [c1]Lauro Langosco di Langosco, Jack Koch, Lee D. Sharkey, Jacob Pfau, David Krueger:
Goal Misgeneralization in Deep Reinforcement Learning. ICML 2022: 12004-12019 - [i3]Sid Black, Lee Sharkey, Léo Grinsztajn, Eric Winsor, Dan Braun, Jacob Merizian, Kip Parker, Carlos Ramón Guevara, Beren Millidge, Gabriel Alfour, Connor Leahy:
Interpreting Neural Networks through the Polytope Lens. CoRR abs/2211.12312 (2022) - [i2]Lee Sharkey:
Circumventing interpretability: How to defeat mind-readers. CoRR abs/2212.11415 (2022) - 2021
- [i1]Jack Koch, Lauro Langosco, Jacob Pfau, James Le, Lee Sharkey:
Objective Robustness in Deep Reinforcement Learning. CoRR abs/2105.14111 (2021)
Coauthor Index
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